Aggregate insider trading: Contrarian beliefs or superior information?

We decompose realized market returns into expected return, unexpected cash-flow news and unexpected discount rate news to test the relation between aggregate market returns and aggregate insider trading. We find that (1) the predictive ability of aggregate insider trading is much stronger than what was reported in earlier studies, (2) aggregate insider trading is strongly related to unexpected cash-flow news, (3) market expectations do not cause insider trading contrary to what others have documented, and (4) aggregate insider trading in firms with high information uncertainty is more likely to be associated with contrarian investment strategy. These results strongly suggest that the predictive ability of aggregate insider trading is because of insider’s ability to predict future cash-flow news rather than from adopting a contrarian investment strategy. These results hold even after we control for non-informative trades and information uncertainty.     

Insider trading,future earnings,and post-earnings announcement drift

This paper examines the association between insider trading prior to quarterly earnings announcements and the magnitude of the post-earnings announcement drift (PEAD). We conjecture and find that insider trades reflect insiders’ private information about the persistence of earnings news. Thus, insider trades can help investors better understand and incorporate the time-series properties of quarterly earnings into stock prices in a timely and unbiased manner, thereby mitigating PEAD. As predicted, PEAD is significantly lower when earnings announcements are preceded by insider trading. The reduction in PEAD is driven by contradictory insider trades (i.e., net buys before large negative earnings news or net sells before large positive earnings news) and is more pronounced in the presence of more sophisticated market participants. Consistent with investors extracting and trading on insiders’ private information, pre-announcement insider trading is associated with smaller market reactions to future earnings news in each of the four subsequent quarters. Overall, our findings indicate insider trading contributes to stock price efficiency by conveying insiders’ private information about future earnings and especially the persistence of earnings news.     

Asset Price Dynamics and Infrequent Feedback Trades

This article combines the continuous arrival of information with the infrequency of trades, and investigates the effects on asset price dynamics of positive and negative-feedback trading. Specifically, we model an economy where stocks and bonds are traded by two types of agents: speculators who maximize expected utility, and feedback traders who mechanically respond to price changes and infrequently submit market orders. We show that positive-feedback strategies increase the volatility of stock returns, and the response of stock prices to dividend news. Conversely, the presence of negative-feedback traders makes stock returns less volatile, and prices less responsive to dividends.     

Investor sentiment and feedback trading: Evidence from the exchange-traded fund markets

This paper extends the standard feedback trading model of Sentana and Wadhwani (1992) by allowing the demand for shares by feedback traders to depend on sentiment. Our empirical analysis of three largest Exchange-Traded Fund (ETF) contracts in the U.S. suggests that there is a significant positive feedback trading in these markets and the intensity of which is generally linked to investor sentiment. Specifically, the level of feedback trading tends to increase when investors are optimistic. In addition, we find that the influence of sentiment on feedback trading varies across market regimes. These results are consistent with the view that feedback trading activity is largely caused by the presence of sentiment-driven noise trading. Overall, the findings are important in understanding the role of sentiment in investment behaviour and market dynamics and are of direct relevance to the regulators and investors in ETF markets.     

An empirical analysis of the role of the trading intensity in information dissemination on the NYSE

In this paper, we use high-frequency data on five frequently traded stocks listed on the New York Stock Exchange (NYSE) in the year 1999 to examine the price impact of trades and its relation to the trading intensity. We show that the distribution of the absolute price change with fast trading first-order stochastically dominates the distribution of the absolute price change with slow trading. Moreover, we find significant causality from the trade characteristics to the trading intensity. Large trades significantly increase the speed of trading, while large returns tend to decrease the trading intensity. We show that this feedback has little impact on the distribution of the price impact of trades.     

Empirical Identification of Non-Informational Trades Using Trading Volume Data

This paper empirically identifies non-informational and informational trades using stock returns and trading volume data of the U.S., Japanese, and U.K. stock markets and five individual firms. We achieve the identification by imposing a restriction from theoretical considerations. Our results show that trading volume is mainly driven by non-informational trades, while stock price movements are primarily driven by informational trades. We also find that, around the 1987 stock market crash, trading volumes due to non-informational trades increased dramatically, while the decline in stock market prices was due mainly to informational trades. Increases in volatilities both in returns and in trading volumes during and after the crash are mainly due to non-informational trades. Regarding the trading volume-serial correlation in the stock returns relationship, we find evidence that is consistent with theoretical predictions that non-informational components can account for high trading volume accompanied by a low serial correlation of stock returns.     

An Investigation of Transactions Data for NYSE Stocks

Using transactions data, the behavior of returns and characteristics of trades at the micro level is examined. A minute-by-minute market return series is formed and tested for normality and autocorrelation. Evidence of differences in return distributions is found among overnight trades, trades during the first 30 minutes following the market opening, trades at the close, and trades during the remainder of the day. The latter distribution is found to be normal. Unusually high returns and standard deviations of returns are found at the beginning and the end of the trading day. When the beginning-and end-of-the-day effects are omitted, autocorrelation in the market return series is reduced substantially. A number of patterns in trading are reported.     

Early Movers Advantage? Evidence from Short Selling during After‐Hours on Earnings Announcement Days

We examine short sellers’ after‐hours trading (AHT) following quarterly earnings announcements released outside of the normal trading hours. Our innovation is to use the actual short trades immediately after the announcements. We find that on these earnings announcement days, there is significant shorting activity in AHT relative to shorting activity both during AHT on nonannouncements days and during regular trading sessions around announcements. Short sellers who trade after‐hours on announcement days earn an excess return of 0.82% and 1.40% during before‐market‐open (BMO) and after‐market‐close (AMC)sessions, respectively. The magnitude of these returns increases to 1.48 (3.92%) for BMO (AMC) earnings announcements with negative surprise. We find that the reactive short selling during AHT has information in predicting future returns. Short sellers’ trades have no predictive power if they wait for the market to open to trade during regular hours. In addition, we find that the weighted price contribution during AHT increases with an increase in after‐hours short selling. Overall, our results suggest that short sellers in AHT are informed. Our findings remain robust using alternative holding periods and after controlling for macroeconomic news announcements during BMO sessions.     

The role of trading intensity estimating the implicit bid–ask spread and determining transitory effects

In this paper, we investigate the information content of trading intensity applying the Madhavan, Richardson and Roomans (1997) structural model to express trading intensity as trading momentum in duration and volume. Using both transactions and intraday data from the Helsinki Stock Exchange Limit Order Bookmarket, we find that momentum in duration and volume enhances the information effect. We reach this conclusion based on the parametric effect determined by the sign and the magnitude of the coefficients associated with the trading intensity variables, the trading effect determined by the ratio of transitory effects to permanent effects, and the economic effect determined by the size of the implicit bid–ask spread. While we find that the implicit bid–ask spread and transitory effects are decreasing toward the end of the trading day in consistency with information models in the literature, there is a surge of trades at the market close, most probably due to information uncertainty at market opening in New York.     

Locals, foreigners, and multi-market trading of equities: Intraday evidence from Thailand

We study stock market orders and trades in a developing country, Thailand, where foreign ownership limits partially segment local and foreign investors into two distinct markets. Some foreigners forgo voting rights and distributions to trade on the “local board”, while some locals forgo such benefits and pay a price premium to trade on the “foreign board”. Regardless of nationality, these cross-market traders typically submit orders when liquidity is high, fill orders at relatively beneficial prices, exploit patterns in stock prices across markets, display profitable holding-period returns, and enhance price discovery. This suggests that skilled, informed trading that affects market quality does not depend on trader nationality.     

Short-run Returns around the Trades of Corporate Insiders on the London Stock Exchange

Previous work examined the long-run profitability of strategies mimicking the trades company directors in the shares of their own company, as a way of testing for market efficiency. The current paper examines patterns in abnormal returns in the days around these trades on the London Stock Exchange.
We find movements in returns that are consistent with directors engaging in short-term market timing. We also report that some types of trades have superior predictive content over future returns. In particular, medium-sized trades are more informative for short-term returns than large ones, consistent with Barclay and Warner's (1993) 'stealth trading' hypothesis whereby informed traders avoid trading in blocks.
Another contribution of this study is to properly adjust the abnormal return estimates for microstructure (spread) transactions costs using daily bid-ask spread data. On a net basis, we find that abnormal returns all but disappear.     

The Market Evaluation of Information in Directors' Trades

The purpose of this paper is to examine the propensity, characteristics and performance of directors' trades. Consistent with prior research we show that on average, directors outperform the market. However, we also find that there exist a large number of trades which do not share these abnormal share price returns and consequently have little information content. This has important consequences for market participants who use director trading activity as a signal for their own trading strategies. Using different measures of directors' trades based on trade characteristics, we report that purchases by directors are more informative than sales. In addition, the number of directors trading within a twenty day window and the percentage of the directors' holding that is being traded are both important factors in the abnormal share price performance following the trade.     

The determinants of quantile autocorrelations: Evidence from the UK

We empirically analyse the cross-sectional determinants of stock return autocorrelations in the UK in different quantiles of conditional return distributions. Autocorrelations in low quantiles are predominantly positive, whereas those in the remaining quantiles are negative. Autocorrelations in different quantiles depend on different sets of firm and trading characteristics: when returns are normal or high, prices react quickly to information, are driven by positive feedback traders, instantaneous news arrivals, and overshoot, trades are predominantly motivated by hedging/liquidity needs, and measured autocorrelations can be biassed by the bid–ask bounce effect and nonsynchronous trading. However, when returns are unusually low, prices are driven by information arriving sequentially and react sluggishly to it, and are influenced by trading on private information and/or negative feedback traders.     

Liquidity Dynamics Around Jumps: The Evidence from the Warsaw Stock Exchange

The aim of our study is to examine the dynamics of trading volume and the number of trades around jumps detected in intraday stock returns. We detect jumps in equally spaced 10-minute returns for most liquid stocks quoted on the Warsaw Stock Exchange within one-year sample period. We match jumps with macroeconomic and firm specific news. We find that only the minority of jumps is associated with public information releases, whereas the majority of them is motivated by liquidity shocks observed in the spreads, volume, and the number of trades. Our findings show that jumps are related to the inability of the market to absorb new and big orders. Liquidity shocks in volatility, volume, and quoted spread are the key drivers accompanying the occurrence of the jumps. Finally, the introduction of a faster and more efficient trading system improves the liquidity by increasing the depth of the market.     

Sell-side analyst heterogeneity and insider trading

This study explores insider trading patterns under different earnings surprises. After controlling for stock market liquidity and earnings announcements returns, we show that insiders sell more aggressively depending on the heterogeneity of analysts whose EPS forecasts are met or beaten to camouflage their trades. Specifically, insiders sell more shares of their company sooner after the publication of earnings when top analysts' forecasts are met or beaten. Consistent with the informed trading literature, insiders strategically select these moments because the stock price impact is low and the legal scrutiny of their trades is minimal. To support this result, we employ an exogenous drop in firms' analyst coverage due to the closure or merger of brokerage houses. Furthermore, in line with the camouflage incentives, by selling after top analysts' forecasts are met or beaten, stock prices adjust slowly to insider trades. Finally, we show that the incentives of insiders to hide their trades are concentrated in opportunistic insiders and members of the top management team, who are more likely to bear the costs of selling shares after positive news.     

INSTITUTIONAL TRADING AND OPENING PRICE BEHAVIOR: EVIDENCE FROM A FAST‐EMERGING MARKET

Using daily and intraday data, we investigate the cross‐sectional relation between stock prices and institutional trading in the Taiwan stock market. Consistent with the investigative herding hypothesis, we find that institutional herding exists because of institutional positive feedback trading behavior rather than following trades made by other institutions, as suggested by the information cascade hypothesis. Moreover, the positive correlation between institutional trade imbalance and stock returns mainly comes from institutional positive feedback trading. The institutional trading decisions rely on returns measured not only over the lagged trading day but also over the opening session during the same day.     

Insider trading patterns

We revisit the information content of stock trading by corporate insiders with an expectation that opportunistic insiders will spread their trades over longer periods of time when they have a longer-lived informational advantage, and trade in a short window of time when their advantage is fleeting. Controlling for the duration of insiders' trading strategies, we find robust new evidence that both insiders' sales and purchases predict abnormal stock returns. In addition, we provide evidence that insiders attempt to preserve their informational advantages and increase their trading profits by disclosing their trades after the market has closed. When insiders report their trades after business hours, they are more likely to engage in longer series of trades, they trade more shares overall, and their trades are associated with larger abnormal returns. Finally, we show how accounting for these trading patterns sharpens screens for corporate insiders who trade on infor- mation.     

Short-term Dynamics in the Cyprus Stock Exchange

Abstract

This paper investigates the short-term dynamics of stock returns in an emerging stock market namely, the Cyprus Stock Exchange (CYSE). Stock returns are modelled as conditionally heteroscedastic processes with time-dependent serial correlation. The conditional variance follows an EGARCH process, while for the conditional mean three nonlinear specifications are tested, namely: (a) the LeBaron exponential autoregressive model; (b) the Sentana and Wadhwani positive feedback trading model; and finally (c) a model that nests both (a) and (b). There is an inverse relationship between volatility and autocorrelation consistent with the findings from several other stock markets, including the US. This pattern could be the manifestation of a certain form of noise trading namely positive feedback trading or, momentum trading strategies. There is little evidence that market declines are followed with higher volatility than market advances, the so-called ‘leverage effect’, that has been observed in almost all developed stock markets. In out of sample forecasts, the nonlinear specifications provide better results in terms of forecasting both first and second moments of the distribution of returns.     

Trade size clustering and the cost of trading at the London Stock Exchange

For the London Stock Exchange, this paper investigates differences in trading costs between market maker (off-book) and order book trades, in the context of clustering in trade sizes and prices. We report several substantial findings. Even after controlling for differences in trade size, the realised spread measure is lower for off-book trades. For the order book, trade size clustering is not associated with differences in transaction costs nor with differences in the information content of trades. For the off-book market, trades in clustered (popular) sizes carry significantly more information than non-clustered trades. Despite the significant differences in the price impact estimates between the order book and off-book, we show that traders placing large orders off-book are still better off than trading via the order book as they benefit from a large discount from the current midpoint price. Additionally, we highlight that price and size clustering tend to occur simultaneously rather than being substitutes in this market setting.     

The dynamics of individual and institutional trading on the Shanghai Stock Exchange

We investigate the daily dynamic relation between returns and institutional and individual trades in the emerging Chinese stock market. Consistent with the hypotheses of trend-chasing and attention-grabbing trading, we find that the response of individual trading to return shocks is much stronger than that of institutional trading, and individuals are net buyers following return shocks. Second, we find that past individual buys and sells have predictive power, whereas past institutional buys and sells have predictive power for market returns in longer horizons. However, both institutional and individual trading activities are more strongly related to past trades than past returns, and individual trading is also influenced by institutional trading. Moreover, we find that institutional trading in the largest quintile leads the trading in the smallest quintile, but no such lead–lag relation is found for individual trades. Finally, we find that the average cumulative abnormal trading volume of individuals is much larger than that of institutions around the firms' earnings announcement, suggesting that less-informed individual investors are more heavily influenced by firm-specific information disclosures and attention-grabbing events.